Pseudo-Perfect and Adaptive Variants of the Metropolis-Hastings Algorithm
with an Independent Candidate Density

We describe and examine an imperfect variant of a perfect sampling
algorithm based on the Metropolis-Hastings algorithm that appears to perform
better than a more traditional approach in terms of speed and accuracy. We
then describe and examine an "adaptive" Metropolis-Hasting algorithm
which generates and updates a self-target candidate density in such a way that
there is no "wrong choice" for an initial candidate density.
Simulation examples are provided.